183 research outputs found

    Automatic artifacts removal from epileptic EEG using a hybrid algorithm

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    Electroencephalogram (EEG) examination plays a very important role in the diagnosis of disorders related to epilepsy in clinic. However, epileptic EEG is often contaminated with lots of artifacts such as electrocardiogram (ECG), electromyogram (EMG) and electrooculogram (EOG). These artifacts confuse EEG interpretation, while rejecting EEG segments containing artifacts probably results in a substantial data loss and it is very time-consuming. The purpose of this study is to develop a novel algorithm for removing artifacts from epileptic EEG automatically. The collected multi-channel EEG data are decomposed into statistically independent components with Independent Component Analysis (ICA). Then temporal and spectral features of each independent component, including Hurst exponent, skewness, kurtosis, largest Lyapunov exponent and frequency-band energy extracted with wavelet packet decomposition, are calculated to quantify the characteristics of different artifact components. These features are imported into trained support vector machine to determine whether the independent components represent EEG activity or artifactual signals. Finally artifact-free EEGs are obtained by reconstructing the signal with artifact-free components. The method is evaluated with EEG recordings acquired from 15 epilepsy patients. Compared with previous work, the proposed method can remove artifacts such as baseline drift, ECG, EMG, EOG, and power frequency interference automatically and efficiently, while retaining important features for epilepsy diagnosis such as interictal spikes and ictal segments

    Numerical simulation and manifold learning for the vibration of molten steel draining from a ladle

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    To ensure the purity of molten steel and maintain the continuity of casting, the slag detection utilizing vibration signals has been widely applied in the continuous casting. Due to the non-stationary and non-linear flow behavior of molten steel, it is hard to construct a reliable criterion to identify the slag entrapment from the vibration signals. In this paper, a numerical simulation model is built to reveal the flow process of molten steel draining from a ladle. By the analysis of the volume fraction, path line and velocity field, the flow state at the moment of slag outflowing is captured. According to the simulated results, a method based on the manifold learning is proposed to deal with the vibration signals. Firstly, the non-stationary vibration signals are decomposed into sub-bands by the continuous wavelet transform and the energy of the signal component at each wavelet scale is calculated to constitute the high dimensional feature space. Then, a manifold learning algorithm called local target space alignment (LTSA) is employed to extract the non-linear principal manifold of the feature space. Finally, the abnormal spectral energy distribution caused by slag entrapment is indicated by the one-dimensional principal manifold. The proposed method is evaluated by the vibration acceleration signals acquired from a steel ladle of 60 tons. Results show that the slag entrapment is exactly and timely identified

    Structural Basis and Functional Mechanism of Lipoprotein in Cholesterol Transport

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    Lipoprotein transports lipids in circulation and is primary driver/modulator of atherosclerosis. Highly dynamics of lipoprotein conformations are crucial to lipid transport along the cholesterol transport pathway, where high-density lipoprotein (HDL), low-density lipoprotein (LDL) and cholesteryl ester transfer protein (CETP) are major players in lipid digestion & transport and the plasma cholesterol metabolism. This chapter covered how do HDL, LDL and CETP induce the metabolisms during cholesterol transport, and summarized recent process in the spatial information of the three lipoproteins, especially the elevations of plasma HDL and LDL, and shine a light on the assembly processes of lipoprotein particles and the substrates dynamics exchanges, for an in-depth understanding on the correlation between various lipoprotein classes and cardiovascular risk

    Fault diagnosis of mechanical drives under non-stationary conditions based on manifold learning of kernel mapping

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    For the detection of mechanical faults under the operating conditions of varying speeds and loads (such as wind turbines, excavators or helicopters, etc.), a new method for extracting the low-dimensional embedding of vibration data sets of mechanical drives under variable operation conditions is proposed. The hypothesis is that the space spanned by a set of vibration signals can be captured in a varying condition, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The embedding manifold generated by vibration signals can be constructed from the feature set of parameters. Taking the variable operation condition into consideration, the kernel mapping is also introduced to improve the identification of submanifolds in terms of the projection distance. With the kernel mapping, the manifold coordinates can accurately capture the differences of the varying operation conditions. Experimental vibration signals obtained from normal and chipped tooth fault of gearbox in varying operation conditions are analyzed in this study. Results show that the proposed method is superior in identifying fault patterns and effective for gearbox condition monitoring

    Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks

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    Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and device computing power can affect its training or communication process in complex network environments. A new network architecture and paradigm with computing-measurable, perceptible, distributable, dispatchable, and manageable capabilities, computing and network convergence (CNC) of 6G networks can effectively support federated learning training and improve its communication efficiency. By guiding the participating devices' training in federated learning based on business requirements, resource load, network conditions, and arithmetic power of devices, CNC can reach this goal. In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning. The experiments address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters. The results show that the method we proposed can (1) cope well with complex network situations (2) effectively balance the delay distribution of participating devices for local training (3) improve the communication efficiency during the transfer of model parameters (4) improve the resource utilization in the network.Comment: 13 pages, 11 figures, accepted by Frontiers of Information Technology & Electronic Engineerin

    A novel Toxoplasma gondii TGGT1_316290 mRNA-LNP vaccine elicits protective immune response against toxoplasmosis in mice

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    Toxoplasma gondii (T. gondii) can infect almost all warm-blooded animals and is a major threat to global public health. Currently, there is no effective drug or vaccine for T. gondii. In this study, bioinformatics analysis on B and T cell epitopes revealed that TGGT1_316290 (TG290) had superior effects compared with the surface antigen 1 (SAG1). TG290 mRNA-LNP was constructed through the Lipid Nanoparticle (LNP) technology and intramuscularly injected into the BALB/c mice, and its immunogenicity and efficacy were explored. Analysis of antibodies, cytokines (IFN-γ, IL-12, IL-4, and IL-10), lymphocytes proliferation, cytotoxic T lymphocyte activity, dendritic cell (DC) maturation, as well as CD4+ and CD8+ T lymphocytes revealed that TG290 mRNA-LNP induced humoral and cellular immune responses in vaccinated mice. Furthermore, T-Box 21 (T-bet), nuclear factor kappa B (NF-kB) p65, and interferon regulatory factor 8 (IRF8) subunit were over-expressed in the TG290 mRNA-LNP-immunized group. The survival time of mice injected with TG290 mRNA-LNP was significantly longer (18.7 ± 3 days) compared with the survival of mice of the control groups (p < 0.0001). In addition, adoptive immunization using 300 μl serum and lymphocytes (5*107) of mice immunized with TG290 mRNA-LNP significantly prolonged the survival time of these mice. This study demonstrates that TG290 mRNA-LNP induces specific immune response against T. gondii and may be a potential toxoplasmosis vaccine candidate for this infection
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